In [2]:
import pandas as pd
import numpy  as np
import datetime as dt
import seaborn 
import matplotlib.pyplot as plt
import plotly.graph_objects as go


#Open a dataBase 
pizzaria_df = pd.read_csv(r"D:\Projetos bancos de dados\Pizzaria\Datafiniti_Pizza_Restaurants_and_the_Pizza_They_Sell_May19.csv")

#cleaning unnecessary data 
pizzaria_df = pizzaria_df.drop(['menuPageURL','postalCode','keys','dateAdded','dateUpdated','menus.dateSeen'],axis=1)

display(pizzaria_df)
id address categories primaryCategories city country latitude longitude menus.amountMax menus.amountMin menus.currency menus.description menus.name name priceRangeCurrency priceRangeMin priceRangeMax province
0 AVz3Y-7h3D1zeR_xDAqm 4203 E Kiehl Ave Pizza,Restaurant,American restaurants,Pizza Pl... Accommodation & Food Services Sherwood US 34.832300 -92.183800 7.98 7.98 USD NaN Cheese Pizza Shotgun Dans Pizza USD 0 25 AR
1 AVweGPFF_7pvs4fzAAzQ 25 E Camelback Rd Pizza,Pizza Place,Restaurants Accommodation & Food Services Phoenix US 33.509266 -112.073044 6.00 6.00 USD NaN Pizza Cookie Sauce Pizza Wine USD 0 25 AZ
2 AVwdRGa9_7pvs4fz4E3K 3703 Paxton Ave Restaurant,Pizza Place,Restaurants Accommodation & Food Services Cincinnati US 39.144883 -84.432685 6.49 6.49 USD a saucelessampcomma double cheese pizza with a... Pizza Blanca Mios Pizzeria USD 0 25 OH
3 AVwdX4psIN2L1WUfvJB1 30495 John R Rd Pizza,Carry-out food,Pizza Place,Restaurants Accommodation & Food Services Madison Heights US 42.516669 -83.106630 5.99 5.99 USD NaN Small Pizza Hungry Howies Pizza USD 25 40 MI
4 AVwdaeTtkufWRAb55pSH 3600 Eastern Ave Pizza,American restaurants,Pizza Place,Pizza e... Accommodation & Food Services Baltimore US 39.286630 -76.566984 5.49 5.49 USD NaN Pizza Sub Spartan Pizzeria USD 0 25 MD
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9995 AVwdKXFYByjofQCxmIyZ 1126 E Lovejoy St Pizza Place Accommodation & Food Services Buffalo US 42.889759 -78.806747 37.10 7.10 USD NaN Super Steak Pizza Carbone's Pizzeria USD 0 25 NY
9996 AVwdKXFYByjofQCxmIyZ 1126 E Lovejoy St Pizza Place Accommodation & Food Services Buffalo US 42.889759 -78.806747 10.10 10.10 USD NaN Large Cheese & 1 Item Pizza Carbone's Pizzeria USD 0 25 NY
9997 AVz6kzsQFcQ3k02bDhwQ 3641 E Main St Pizza Place,Restaurants Accommodation & Food Services Saint Charles US 41.921300 -88.275300 8.45 8.45 USD NaN Individual 6" Pepperoni Stuffed Deep Dish Pizza Giordano's Pizza USD 0 25 IL
9998 AVz6kzsQFcQ3k02bDhwQ 3641 E Main St Pizza Place,Restaurants Accommodation & Food Services Saint Charles US 41.921300 -88.275300 8.45 8.45 USD NaN Individual 6" Cheese Stuffed Deep Dish Pizza Giordano's Pizza USD 0 25 IL
9999 AVwdG9zTkufWRAb52Y2A 7460 W Lake Mead Blvd Pizza Place,Restaurant,Fast Food,Pizza Accommodation & Food Services Las Vegas US 36.196180 -115.256240 8.79 8.79 USD NaN King Arthur's Supreme Pizza Round Table Pizza USD 0 25 NV

10000 rows × 18 columns

In [2]:
#Question 1:
'''
What are the categories of resturants? 
'''
categories = pizzaria_df['primaryCategories'].value_counts().reset_index().rename(columns ={'index' :'Primary Categories','primaryCategories':'Values'},\
                                                                                  index={0:1,1:2,2:3,3:4,4:5,5:6,6:7,7:8})
display(categories)
'''
Analysis:
you can see that there are several pizzerias that have different categories, such as
they serve pizzas and function as restaurants 
'''
Primary Categories Values
1 Accommodation & Food Services 9909
2 Management of Companies & Enterprises 43
3 Accommodation & Food Services,Management of Co... 23
4 Retail,Accommodation & Food Services 11
5 Retail 9
6 Wholesale Trade,Accommodation & Food Services 2
7 Educational Services,Accommodation & Food Serv... 2
8 Wholesale Trade,Accommodation & Food Services,... 1
Out[2]:
'\nAnalysis:\nyou can see that there are several pizzerias that have different categories, such as\nthey serve pizzas and function as restaurants \n'
In [21]:
#Question 2:
'''
What are the 10 cities with the most pizzerias by province?  
'''
cities_for_provinces = pizzaria_df[['city','province']].value_counts().reset_index().rename(columns={0:'restaurants by city'},\
                                                                                     index={0:1,1:2,2:3,3:4,4:5,5:6,7:8,9:10})

cities_for_provinces_plot = pizzaria_df[['city']].value_counts().head(10).reset_index().rename(columns={0:'restaurants by city'},\
                                                                                               index={0:1,1:2,2:3,3:4,4:5,5:6,7:8,9:10}).plot('city','restaurants by city',kind='bar')

display(cities_for_provinces.head(10))
print('==='*40)
city province restaurants by city
1 New York NY 655
2 Brooklyn NY 460
3 Los Angeles CA 193
4 Buffalo NY 178
5 Philadelphia PA 140
6 Seattle WA 135
6 Chicago IL 133
8 Flushing NY 124
8 San Francisco CA 110
10 Bronx NY 88 
========================================================================================================================
In [18]:
#Question 3:
'''
top 10 frequented provinces 
'''
provinces = pizzaria_df['province'].value_counts().reset_index().rename(columns={'index':'provinces','province':'frequency '},\
                                                                    index={0:1,1:2,2:3,3:4,4:5,5:6,7:8,9:10})
plot_df= pizzaria_df['province'].value_counts().head(10).reset_index().rename(columns={'index':'provinces','province':'frequency'},\
                                                                      index={0:1,1:2,2:3,3:4,4:5,5:6,7:8,9:10}).plot('provinces','frequency',kind ='barh')

display(provinces.head(10))
                                                               
print('==='*40)
provinces frequency
1 NY 2640
2 CA 1386
3 PA 814
4 FL 601
5 TX 485
6 IL 381
6 MI 366
8 OH 324
8 AZ 288
10 WA 262 
========================================================================================================================
In [22]:
#Question 4:
'''

'''
print('total franchises in the data set: {}'.format(len(np.unique(pizzaria_df['id']))))
restaurant_list = pizzaria_df['name'].value_counts().reset_index().rename(columns ={'index':'Name','name':'number of branches'},\
                                                                          index={0:1,1:2,2:3,3:4,4:5,5:6,7:8,9:10})

pizzaria_df['name'].value_counts().head(10).reset_index().rename(columns ={'index':'Name','name':'Values'}).plot('Name','Values',kind ='barh')
                                                       
display(restaurant_list.head(10))
print('==='*40) 

total franchises in the data set: 2285
Name number of branches
1 California Pizza Kitchen 261
2 Papa Murphy's 208
3 Papa John's Pizza 124
4 Papa Murphys 69
5 Gino's Pizzeria 68
6 Pirates Pizza 55
6 Olympia Pizza and Spaghetti House 54
8 Round Table Pizza 54
8 Domino's Pizza 54
10 Pizza Hut 51 
========================================================================================================================
In [23]:
#Question 5:
'''
Top 10 Pizza flavors 
'''
#Removed the 'pizza' line as it is generalized 
menu_pizza_df = pizzaria_df['menus.name'].value_counts().reset_index()
menu_pizza_df = menu_pizza_df.drop(menu_pizza_df.index[[4]])
menu_pizza_df = menu_pizza_df.rename(columns ={'index':'Name'},index={0:1,1:2,2:3,3:4})

display(menu_pizza_df.head(10))
print('==='*40) 

#I plotted by stages to remove the 'pizza' line 
menu_pizza_grapic_df = pizzaria_df['menus.name'].value_counts().head(10).reset_index().rename(columns ={'index':'Name'})                                                      
menu_pizza_grapic_df = menu_pizza_grapic_df.drop(menu_pizza_grapic_df.index[[4]])
menu_pizza_grapic_df.plot('Name','menus.name',kind='bar')
Name menus.name
1 Cheese Pizza 299
2 White Pizza 225
3 Hawaiian Pizza 149
4 Buffalo Chicken Pizza 112
5 Margherita Pizza 104
6 Veggie Pizza 95
7 Bbq Chicken Pizza 81
8 Greek Pizza 72
9 Pizza Sub 71
10 Pizza Burger 70 
========================================================================================================================
Out[23]:
<AxesSubplot:xlabel='Name'>
In [3]:
#Question 6:
'''
where can i find cheese pizza: 
'''
chesse_pizza_df =  pizzaria_df[pizzaria_df['menus.name'] == 'Cheese Pizza']
chesse_pizza_df.drop_duplicates('id',inplace = True)
chesse_pizza_df.shape
fig = go.Figure(data=go.Scattergeo(
        lon = chesse_pizza_df['longitude'],
        lat = chesse_pizza_df['latitude'],
        text = chesse_pizza_df['name'] + ' :-' + chesse_pizza_df['province'],
        mode = 'markers',
    
        marker_color = 'red',
        ))

fig.update_layout(
        title = 'Cheese Pizza',
        geo_scope='usa',
    )
fig.show()

C:\Users\Pichau\anaconda3\lib\site-packages\pandas\util\_decorators.py:311: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return func(*args, **kwargs)
In [ ]:
#Question 7:
'''
where ca